{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _*NaH dissociation curve using VQE with UCCSD*_\n",
    "\n",
    "This notebook demonstrates using the Qiskit Chemistry to plot graphs of the ground state energy of the Sodium Hydride (NaH) molecule over a range of inter-atomic distances using VQE and UCCSD. It is compared to the same energies as computed by the NumPyMinimumEigensolver.\n",
    "\n",
    "_*Note: this molecule is larger than the similar LiH and this notebook can take a while to run.*_\n",
    "\n",
    "This notebook has been written to use the PYSCF chemistry driver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing step 23 --- complete\n",
      "Distances:  [1.   1.1  1.2  1.3  1.4  1.5  1.6  1.7  1.8  1.9  2.   2.1  2.2  2.3\n",
      " 2.4  2.5  2.75 3.   3.25 3.5  3.75 4.   4.25 4.5 ]\n",
      "Energies: [[-160.05849063 -160.15699836 -160.22568735 -160.27202139 -160.30172252\n",
      "  -160.31895083 -160.32675432 -160.32741528 -160.32269878 -160.31400273\n",
      "  -160.30245852 -160.28899051 -160.27435538 -160.2591661  -160.2439109\n",
      "  -160.22897181 -160.19475711 -160.16708758 -160.14746338 -160.13627128\n",
      "  -160.13114984 -160.12788151 -160.12587996 -160.06078205]\n",
      " [-160.05849084 -160.15699856 -160.22568741 -160.2720216  -160.30172261\n",
      "  -160.31895199 -160.32675458 -160.32741545 -160.32269886 -160.31400297\n",
      "  -160.30245861 -160.28899063 -160.27435552 -160.25916618 -160.24391112\n",
      "  -160.22897222 -160.19475719 -160.16708762 -160.14746354 -160.13627173\n",
      "  -160.13150727 -160.12988489 -160.12941537 -160.12738873]]\n",
      "Hartree-Fock energies: [-160.04320295 -160.14360744 -160.21336733 -160.26022033 -160.29007462\n",
      " -160.30721237 -160.31476208 -160.31507193 -160.30995602 -160.30085169\n",
      " -160.28891892 -160.2751014  -160.26016389 -160.24471683 -160.2292359\n",
      " -160.21408033 -160.17913095 -160.14978812 -160.12634274 -160.10810649\n",
      " -160.09400858 -160.08298959 -160.07419396 -160.0607817 ]\n",
      "Dipoles: [[ 2.97124823  3.4760568   3.89545714  4.26391478  4.59602472  4.90977508\n",
      "   5.21848513  5.52130626  5.82181283  6.11915437  6.41401666  6.70106732\n",
      "   6.97515357  7.22566557  7.44609762  7.62641864  7.79601453  7.18510394\n",
      "   5.30078459  2.68459082  1.70804255  1.7740377   1.76461456 21.57731567]\n",
      " [ 2.97335246  3.47789485  3.89561999  4.26006188  4.59374084  4.91025573\n",
      "   5.21772576  5.52078168  5.82151088  6.11992744  6.41423476  6.70095324\n",
      "   6.97491033  7.22906568  7.45413201  7.63797444  7.80073442  7.19343854\n",
      "   5.31627389  2.65735429  0.91782198  0.26885135  0.07470177  0.0219034 ]]\n",
      "VQE num evaluations: [211. 211. 186. 185. 160. 159. 211. 236. 212. 186. 186. 184. 184. 186.\n",
      " 211. 211. 236. 262. 286. 338. 391. 339. 364.  28.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab\n",
    "import copy\n",
    "from qiskit import BasicAer\n",
    "from qiskit.aqua import QuantumInstance\n",
    "from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE\n",
    "from qiskit.aqua.components.optimizers import SLSQP\n",
    "from qiskit.chemistry.components.initial_states import HartreeFock\n",
    "from qiskit.chemistry.components.variational_forms import UCCSD\n",
    "from qiskit.chemistry.drivers import PySCFDriver\n",
    "from qiskit.chemistry.core import Hamiltonian, QubitMappingType\n",
    "\n",
    "molecule = 'H .0 .0 -{0}; Na .0 .0 {0}'\n",
    "algorithms = ['VQE', 'NumPyMinimumEigensolver']\n",
    "\n",
    "pts  = [x * 0.1  for x in range(10, 25)]\n",
    "pts += [x * 0.25 for x in range(10, 18)]\n",
    "pts += [4.5]\n",
    "energies = np.empty([len(algorithms), len(pts)])\n",
    "hf_energies = np.empty(len(pts))\n",
    "distances = np.empty(len(pts))\n",
    "dipoles     = np.empty([len(algorithms), len(pts)])\n",
    "eval_counts = np.empty(len(pts))\n",
    "\n",
    "print('Processing step __', end='')\n",
    "for i, d in enumerate(pts):\n",
    "    print('\\b\\b{:2d}'.format(i), end='', flush=True)\n",
    "    for j in range(len(algorithms)):\n",
    "        driver = PySCFDriver(atom=molecule.format(d/2), basis='sto3g')\n",
    "        qmolecule = driver.run()\n",
    "        operator =  Hamiltonian(qubit_mapping=QubitMappingType.PARITY,\n",
    "                                two_qubit_reduction=True,\n",
    "                                freeze_core=True, orbital_reduction=[])\n",
    "        qubit_op, aux_ops = operator.run(qmolecule)\n",
    "        if algorithms[j] == 'NumPyMinimumEigensolver':\n",
    "            result = NumPyMinimumEigensolver(qubit_op, aux_operators=aux_ops).run()\n",
    "        else:\n",
    "            optimizer = SLSQP(maxiter=2500)\n",
    "            initial_state = HartreeFock(operator.molecule_info['num_orbitals'],\n",
    "                                        operator.molecule_info['num_particles'],\n",
    "                                        qubit_mapping=operator._qubit_mapping,\n",
    "                                        two_qubit_reduction=operator._two_qubit_reduction)\n",
    "            var_form = UCCSD(num_orbitals=operator.molecule_info['num_orbitals'],\n",
    "                            num_particles=operator.molecule_info['num_particles'],\n",
    "                            initial_state=initial_state,\n",
    "                            qubit_mapping=operator._qubit_mapping,\n",
    "                            two_qubit_reduction=operator._two_qubit_reduction)\n",
    "            algo = VQE(qubit_op, var_form, optimizer, aux_operators=aux_ops)\n",
    "            result = algo.run(QuantumInstance(BasicAer.get_backend('statevector_simulator')))\n",
    "            eval_counts[i] = result.optimizer_evals\n",
    "                              \n",
    "        result = operator.process_algorithm_result(result)\n",
    "        energies[j][i] = result.energy\n",
    "        hf_energies[i] = result.hartree_fock_energy\n",
    "        dipoles[j][i]  = result.total_dipole_moment / 0.393430307\n",
    "\n",
    "    distances[i] = d\n",
    "print(' --- complete')\n",
    "\n",
    "print('Distances: ', distances)\n",
    "print('Energies:', energies)\n",
    "print('Hartree-Fock energies:', hf_energies)\n",
    "print('Dipoles:', dipoles)\n",
    "print('VQE num evaluations:', eval_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, hf_energies, label='Hartree-Fock')\n",
    "for j in range(len(algorithms)):\n",
    "    pylab.plot(distances, energies[j], label=algorithms[j])\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('NaH Ground State Energy')\n",
    "pylab.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, np.subtract(hf_energies, energies[1]), label='Hartree-Fock')\n",
    "pylab.plot(distances, np.subtract(energies[0], energies[1]), label='VQE')\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('Energy difference from NumPyMinimumEigensolver')\n",
    "pylab.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for j in reversed(range(len(algorithms))):\n",
    "    pylab.plot(distances, dipoles[j], label=algorithms[j])\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Moment in debye')\n",
    "pylab.title('NaH Dipole Moment')\n",
    "pylab.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, eval_counts, '-o', color=[0.8500, 0.3250, 0.0980], label='VQE')\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Evaluations')\n",
    "pylab.title('VQE number of evaluations')\n",
    "pylab.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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